2) What I am sure I do NOT know (It’s a swan, but I can NOT tell if it is white or black.)

3) What is so unknowable, that I cannot even postulate the problem (Do not see any swans on the horizon)

I am increasingly convinced that one or more firms will have meltdowns like MF Global and JP Morgan Chase. I know classify this is as “What I think I know.” It is NOT a mathematically provable point, so each investor will have to decide on their own.

The New York Times published a short story length article in this week’s Magazine that reinforces my belief. The article is about Ina Drew, who was the Chief Investment Officer of JP Morgan Chase when the “London Whale” trade went bad. [1] The story is entitled The Woman Who Took the Fall for JPMorgan Chase.

The London Whale trade was a single trade that the London office put on that is estimated to result in a $ 6 billion loss. The loss is manageable for JPMC and they will survive mostly with a damaged reputation. Obviously, the same is not true for MF Global where a single trade gone wrong killed the firm.

There are several themes in the article and it does NOT conclude that another large bad trade is inevitable. The article is more a matter of fact presentation about the history of Ina and the London Whale trade.

The central themes I picked out from the story are:

1) JPMC still maintains that trade was a HEDGE against other their loan portfolio. They steadfastly maintain it was NOT a speculative bet by their proprietary trading desk. Hence any regulation that allows banks to hedge their bets will still have the same risk. If you want to remove this risk from a TBTF and/or FDIC bank, you have to do a much stronger split of responsibilities. It is NOT clear to me if Glass-Steagall would have prevented this or not. As I understand Dodd-Frank, it would NOT have prevented this trade.

“was that by the time we were finished, we were making more than 50 percent of the bank’s profits.

No bank CEO is going to stand up to his board and say “I have a great plan for us to shut down our trading operation and cut earnings in half.”

3) Some of these financial Mensa members still 100% believe in models. Many of the “quants” have backgrounds in math and/or physics. Drew’s team had all graduated from well-regarded schools, but unlike Edsparr’s group, they did not have Ph.D.’s in applied math; they weren’t M.I.T. graduates or physicists from Caltech

They approach finance like it is a hard science where a law of physics is sacred. Their belief is that they can develop a model that will include all possible outcomes. When a 10 sigma or black swan event arrives, they are shocked when the model fails.

To try to mitigate that very human dynamic, banks also rely on a variety of statistical models, including those known as “value at risk” models, which theoretically provide bankers with a certain degree of probability about how much they could stand to lose on any given day under adverse circumstances. Those V.A.R. models did little to help bankers when the unforeseeable happened in 2008, which is why they are generally viewed with some skepticism these days. Sometimes the models miss key information, sometimes the people who use them miss what the models are telling them and sometimes traders manage to work around them.

The trade to short A/long B was derived by quantitative methods. In addition to that the Value At Risk was ALSO derived from a model. VAR is intended to show what the maximum loss for a trade can be in any given day. It is generally regarded as being the equivalent of simulating a 2 sigma (95%) or a 3 sigma (99.7%) event. Obviously this assumes that the range of results has a Gaussian distribution. But what happens if the distribution is NOT Gaussian?

4) Access to free ~0% funds makes investors willing to take more risks. This is exactly the result that Ben and the Fed want.

Iksil’s colleagues liked him, but he was not popular among some Wall Street dealers who brokered his trades. The London group had a reputation for using the weight of the bank to muscle the rest of the market and for being a little arrogant. “They thought they were geniuses,” said a hedge-fund manager on the other side of the trade that ultimately brought Iksil down. “They just had access to cheaper capital than everyone else, because they worked at JPMorgan,” that manager said. “It’s sort of like race cars — everyone else is in a Camry, and you’re in Porsche, and you think you’re the best driver.”

5) A lot of the story focuses on the challenges that Ina faced because of her sex over the years. It is NOT clear how or if the sexism has improved over the years. What is clear is that darn few women are in influential positions on Wall Street.

In the early 1980s, Chemical was making a push to hire women as traders, but that did not mean the workplace was particularly enlightened. Dina Dublon, who was one of Drew’s closest colleagues, joined the bank the same month that Drew did. “To say the environment was not welcoming to women is an understatement,” says Dublon, who rose through the ranks alongside Drew and retired in 2004. Dublon sat next to Drew in the early days, and the women went through packs of cigarettes every day, a cloud of smoke hovering around their desks like a barrier to the barrage of tasteless jokes.

BOTTOM LINE IMO is that firms will continue to see large trades that go bad by “surprise.” The odds of a regulatory answer seem low. The odds of a Federal Reserve bailout seem very high if the bank is TBTF. A whole flock of black swans went in for bleaching and are now white swans. We should not be surprised if a firm blows itself up again when a white swan lands close by.

You might come to entirely different conclusions after reading this story. If you are interested in swan detection techniques, you might want to read the story.